
'''simple docstring'''
import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
						from PIL import Image
else:


						class 			SCREAMING_SNAKE_CASE__      :







										@staticmethod
										def 						UpperCAmelCase_    (       *A_						,				**A_     )->		Union[str, Any]:







																	'''simple docstring'''






																	pass


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):
				lowerCAmelCase_     			=							MODEL_FOR_OBJECT_DETECTION_MAPPING







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     ObjectDetectionPipeline(model=_lowercase						,				image_processor=_lowercase     )
											return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







				def 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'						,				threshold=0.0     )

											self.assertGreater(len(_lowercase     )						,				0     )
											for detected_object in outputs:
																		self.assertEqual(
																		    _lowercase						,				{
																		        'score': ANY(_lowercase     ),
																		        'label': ANY(_lowercase     ),
																		        'box': {'xmin': ANY(_lowercase     ), 'ymin': ANY(_lowercase     ), 'xmax': ANY(_lowercase     ), 'ymax': ANY(_lowercase     )},
																		    }						,				)

											import datasets

											UpperCamelCase   			=     datasets.load_dataset('hf-internal-testing/fixtures_image_utils'						,				'image'						,				split='test'     )

											UpperCamelCase   			=     [
											    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     ),
											    """http://images.cocodataset.org/val2017/000000039769.jpg""",
											    # RGBA
											    dataset[0]["""file"""],
											    # LA
											    dataset[1]["""file"""],
											    # L
											    dataset[2]["""file"""],
											]
											UpperCamelCase   			=     object_detector(_lowercase						,				threshold=0.0     )

											self.assertEqual(len(_lowercase     )						,				len(_lowercase     )     )
											for outputs in batch_outputs:
																		self.assertGreater(len(_lowercase     )						,				0     )
																		for detected_object in outputs:
																									self.assertEqual(
																									    _lowercase						,				{
																									        'score': ANY(_lowercase     ),
																									        'label': ANY(_lowercase     ),
																									        'box': {'xmin': ANY(_lowercase     ), 'ymin': ANY(_lowercase     ), 'xmax': ANY(_lowercase     ), 'ymax': ANY(_lowercase     )},
																									    }						,				)







				@require_tf
				@unittest.skip('Object detection not implemented in TF'     )
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											pass







				@require_torch
				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     """hf-internal-testing/tiny-detr-mobilenetsv3"""

											UpperCamelCase   			=     AutoModelForObjectDetection.from_pretrained(_lowercase     )
											UpperCamelCase   			=     AutoFeatureExtractor.from_pretrained(_lowercase     )
											UpperCamelCase   			=     ObjectDetectionPipeline(model=_lowercase						,				feature_extractor=_lowercase     )

											UpperCamelCase   			=     object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'						,				threshold=0.0     )

											self.assertEqual(
											    nested_simplify(_lowercase						,				decimals=4     )						,				[
											        {'score': 0.3_376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
											        {'score': 0.3_376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
											    ]						,				)

											UpperCamelCase   			=     object_detector(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]						,				threshold=0.0						,				)

											self.assertEqual(
											    nested_simplify(_lowercase						,				decimals=4     )						,				[
											        [
											            {'score': 0.3_376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
											            {'score': 0.3_376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
											        ],
											        [
											            {'score': 0.3_376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
											            {'score': 0.3_376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
											        ],
											    ]						,				)







				@require_torch
				@slow
				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     """facebook/detr-resnet-50"""

											UpperCamelCase   			=     AutoModelForObjectDetection.from_pretrained(_lowercase     )
											UpperCamelCase   			=     AutoFeatureExtractor.from_pretrained(_lowercase     )
											UpperCamelCase   			=     ObjectDetectionPipeline(model=_lowercase						,				feature_extractor=_lowercase     )

											UpperCamelCase   			=     object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'     )
											self.assertEqual(
											    nested_simplify(_lowercase						,				decimals=4     )						,				[
											        {'score': 0.9_982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
											        {'score': 0.9_960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
											        {'score': 0.9_955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
											        {'score': 0.9_988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
											        {'score': 0.9_987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
											    ]						,				)

											UpperCamelCase   			=     object_detector(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]     )
											self.assertEqual(
											    nested_simplify(_lowercase						,				decimals=4     )						,				[
											        [
											            {'score': 0.9_982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
											            {'score': 0.9_960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
											            {'score': 0.9_955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
											            {'score': 0.9_988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
											            {'score': 0.9_987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
											        ],
											        [
											            {'score': 0.9_982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
											            {'score': 0.9_960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
											            {'score': 0.9_955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
											            {'score': 0.9_988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
											            {'score': 0.9_987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
											        ],
											    ]						,				)







				@require_torch
				@slow
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     """facebook/detr-resnet-50"""

											UpperCamelCase   			=     pipeline('object-detection'						,				model=_lowercase     )

											UpperCamelCase   			=     object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'     )
											self.assertEqual(
											    nested_simplify(_lowercase						,				decimals=4     )						,				[
											        {'score': 0.9_982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
											        {'score': 0.9_960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
											        {'score': 0.9_955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
											        {'score': 0.9_988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
											        {'score': 0.9_987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
											    ]						,				)

											UpperCamelCase   			=     object_detector(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]     )
											self.assertEqual(
											    nested_simplify(_lowercase						,				decimals=4     )						,				[
											        [
											            {'score': 0.9_982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
											            {'score': 0.9_960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
											            {'score': 0.9_955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
											            {'score': 0.9_988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
											            {'score': 0.9_987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
											        ],
											        [
											            {'score': 0.9_982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
											            {'score': 0.9_960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
											            {'score': 0.9_955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
											            {'score': 0.9_988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
											            {'score': 0.9_987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
											        ],
											    ]						,				)







				@require_torch
				@slow
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     0.9_985
											UpperCamelCase   			=     """facebook/detr-resnet-50"""

											UpperCamelCase   			=     pipeline('object-detection'						,				model=_lowercase     )

											UpperCamelCase   			=     object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'						,				threshold=_lowercase     )
											self.assertEqual(
											    nested_simplify(_lowercase						,				decimals=4     )						,				[
											        {'score': 0.9_988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
											        {'score': 0.9_987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
											    ]						,				)







				@require_torch
				@require_pytesseract
				@slow
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     """Narsil/layoutlmv3-finetuned-funsd"""
											UpperCamelCase   			=     0.9_993

											UpperCamelCase   			=     pipeline('object-detection'						,				model=_lowercase						,				threshold=_lowercase     )

											UpperCamelCase   			=     object_detector(
											    'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'     )
											self.assertEqual(
											    nested_simplify(_lowercase						,				decimals=4     )						,				[
											        {'score': 0.9_993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
											        {'score': 0.9_993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
											    ]						,				)







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[Any] 							= [
			    """encoder.version""",
			    """decoder.version""",
			    """model.encoder.version""",
			    """model.decoder.version""",
			    """decoder.output_projection.weight""",
			    """_float_tensor""",
			    """encoder.embed_positions._float_tensor""",
			    """decoder.embed_positions._float_tensor""",
			]
			for k in ignore_keys:
						state_dict.pop(a__  ,	a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_							,					a_						:						List[Any] 							= emb.weight.shape
			a_						:						Dict 							= nn.Linear(a__  ,	a__  ,	bias=a__)
			a_						:						str 							= emb.weight.data
			return lin_layer


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= torch.load(a__  ,	map_location="""cpu""")
			a_						:						int 							= mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
			a_						:						Dict 							= mam_aaa["""model"""]
			remove_ignore_keys_(a__)
			a_						:						List[str] 							= state_dict["""encoder.embed_tokens.weight"""].shape[0]

			a_						:						Union[str, Any] 							= MaMaaaConfig(
			    vocab_size=a__  ,	max_position_embeddings=1_0_2_4  ,	encoder_layers=args.encoder_layers  ,	decoder_layers=args.decoder_layers  ,	encoder_attention_heads=args.encoder_attention_heads  ,	decoder_attention_heads=args.decoder_attention_heads  ,	encoder_ffn_dim=args.encoder_ffn_embed_dim  ,	decoder_ffn_dim=args.decoder_ffn_embed_dim  ,	d_model=args.encoder_embed_dim  ,	encoder_layerdrop=args.encoder_layerdrop  ,	decoder_layerdrop=args.decoder_layerdrop  ,	dropout=args.dropout  ,	attention_dropout=args.attention_dropout  ,	activation_dropout=args.activation_dropout  ,	activation_function="""relu"""  ,	)

			a_						:						Union[str, Any] 							= state_dict["""decoder.embed_tokens.weight"""]
			a_						:						List[Any] 							= MaMaaaForConditionalGeneration(a__)
			model.model.load_state_dict(a__  ,	strict=a__)
			a_						:						Any 							= make_linear_from_emb(model.model.shared)

			return model


if __name__ == "__main__":
	__snake_case				:      Union[str, Any]   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
	parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	__snake_case				:      int   		=			parser.parse_args()
	__snake_case				:      Any   		=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
	model.save_pretrained(args.pytorch_dump_folder_path)


